Python库 | fastcore-1.2.5.tar.gz

# Welcome to fastcore > Python goodies to make your coding faster, easier, and more maintainable Python is a powerful, dynamic language. Rather than bake everything into the language, it lets the programmer customize it to make it work for them. `fastcore` uses this flexibility to add to Python features inspired by other languages we've loved, like multiple dispatch from Julia, mixins from Ruby, and currying, binding, and more from Haskell. It also adds some "missing features" and clean up some rough edges in the Python standard library, such as simplifying parallel processing, and bringing ideas from NumPy over to Python's `list` type. ## Installing To install fastcore run: `conda install fastcore` (if you use Anaconda, which we strongly recommend) or `pip install fastcore`. For an [editable install](https://stackoverflow.com/questions/35064426/when-would-the-e-editable-option-be-useful-with-pip-install), clone this repo and run: `pip install -e ".[dev]"`. ## A tour `fastcore` contains many features. See the [docs](https://fastcore.fast.ai) for all the details, which cover the modules provided: - `test`: Simple testing functions - `foundation`: Mixins, delegation, composition, and more - `utils`: Utility functions to help with functional-style programming, parallel processing, and more - `dispatch`: Multiple dispatch methods - `transform`: Pipelines of composed partially reversible transformations Here's a (somewhat) quick tour of a few higlights, showing examples from each of these modules. ### Documentation All fast.ai projects, including this one, are built with [nbdev](https://nbdev.fast.ai), which is a full literate programming environment built on Jupyter Notebooks. That means that every piece of documentation, including the page you're reading now, can be accessed as interactive Jupyter notebooks. In fact, you can even grab a link directly to a notebook running interactively on Google Colab - if you want to follow along with this tour, click the link below, or click the badge at the top of the page: ``` colab_link('index') ``` [Open `index` in Colab](https://colab.research.google.com/github/fastai/fastcore/blob/master/nbs/index.ipynb) The full docs are available at [fastcore.fast.ai](https://fastcore.fast.ai). The code in the examples and in all fast.ai libraries follow the [fast.ai style guide](https://docs.fast.ai/dev/style.html). In order to support interactive programming, all fast.ai libraries are designed to allow for `import *` to be used safely, particular by ensuring that [`__all__`](https://riptutorial.com/python/example/2894/the---all---special-variable) is defined in all packages. In order to see where a function is from, just type it: ``` coll_repr ``` <function fastcore.foundation.coll_repr(c, max_n=10)> For more details, including a link to the full documentation and source code, use `doc`, which pops up a window with this information: ```python doc(coll_repr) ``` <img width="499" src="nbs/images/att_00000.png" align="left"> The documentation also contains links to any related functions or classes, which appear like this: `coll_repr` (in the notebook itself you will just see a word with back-ticks around it; the links are auto-generated in the documentation site). The documentation will generally show one or more examples of use, along with any background context necessary to understand them. As you'll see, the examples for each function and method are shown as tests, rather than example outputs, so let's start by explaining that. ### Testing fastcore's testing module is designed to work well with [nbdev](https://nbdev.fast.ai), which is a full literate programming environment built on Jupyter Notebooks. That means that your tests, docs, and code all live together in the same notebook. fastcore and nbdev's approach to testing starts with the premise that all your tests should pass. If one fails, no more tests in a notebook are run. Tests look like this: ``` test_eq(coll_repr(range(1000), 5), '(#1000) [0,1,2,3,4...]') ``` That's an example from the docs for `coll_repr`. As you see, it's not showing you the output directly. Here's what that would look like: ``` coll_repr(range(1000), 5) ``` '(#1000) [0,1,2,3,4...]' So, the test is actually showing you what the output looks like, because if the function call didn't return `'(#1000) [0,1,2,3,4...]'`, then the test would have failed. So every test shown in the docs is also showing you the behavior of the library --- and vice versa! Test functions always start with `test_`, and then follow with the operation being tested. So `test_eq` tests for equality (as you saw in the example above). This includes tests for equality of arrays and tensors, lists and generators, and many more: ``` test_eq([0,1,2,3], np.arange(4)) ``` When a test fails, it prints out information about what was expected: ```python test_eq([0,1,2,3], np.arange(3)) ``` ``` ---- AssertionError: ==: [0, 1, 2, 3] [0 1 2] ``` If you want to check that objects are the same type, rather than the just contain the same collection, use `test_eq_type`. You can test with any comparison function using `test`, e.g test whether an object is less than: ``` test(2, 3, operator.lt) ``` You can even test that exceptions are raised: ``` def divide_zero(): return 1/0 test_fail(divide_zero) ``` ...and test that things are printed to stdout: ``` test_stdout(lambda: print('hi'), 'hi') ``` ### Foundations fast.ai is unusual in that we often use [mixins](https://en.wikipedia.org/wiki/Mixin) in our code. Mixins are widely used in many programming languages, such as Ruby, but not so much in Python. We use mixins to attach new behavior to existing libraries, or to allow modules to add new behavior to our own classes, such as in extension modules. One useful example of a mixin we define is `Path.ls`, which lists a directory and returns an `L` (an extended list class which we'll discuss shortly): ``` p = Path('images') p.ls() ``` (#6) [Path('images/att_00007.png'),Path('images/att_00000.png'),Path('images/puppy.jpg'),Path('images/att_00006.png'),Path('images/mnist3.png'),Path('images/att_00005.png')] You can easily add you own mixins with the `patch` [decorator](https://realpython.com/primer-on-python-decorators/), which takes advantage of Python 3 [function annotations](https://www.python.org/dev/peps/pep-3107/#parameters) to say what class to patch: ``` @patch def num_items(self:Path): return len(self.ls()) p.num_items() ``` 6 We also use `**kwargs` frequently. In python `**kwargs` in a parameter like means "*put any additional keyword arguments into a dict called `kwargs`*". Normally, using `kwargs` makes an API quite difficult to work with, because it breaks things like tab-completion and popup lists of signatures. `utils` provides `use_kwargs` and `delegates` to avoid this problem. See our [detailed article on delegation](https://www.fast.ai/2019/08/06/delegation/) on this topic. `GetAttr` solves a similar problem (and is also discussed in the article linked above): it's allows you to use Python's exceptionally useful `__getattr__` magic method, but avoids the problem that normally in Python tab-completion and docs break when using this. For instance, you can see here that Python's `dir` function, which is used to find the attributes of a python object, finds everything inside the `self.default` attribute here: ``` class Author: def __init__(self, name): self.name = name class ProductPage(GetAttr): _default = 'author' def __init__(self,author,price,cost): self.author,self.price,self.cost = author,price,cost p = ProductPage(Author("Jeremy"), 1.50, 0.50) [o for o in dir(p) if not o.startswith('_')] ``` ['author', 'cost', 'name', 'price'] Looking at that `ProductPage` example, it's rather verbose and duplicates a lot of attribute names, which can lead to bugs later if you change them only in one place. `fastcore` provides `sto